Start, monitor, and track run history

The Azure Machine Learning SDK for Python, Machine Learning CLI, and Azure Machine Learning studio provide various methods to monitor, organize, and track your runs for training and experimentation. Your ML run history is an important part of an explainable and repeatable ML development process.

This article shows how to do the following tasks:

  • Monitor run performance.
  • Add run display name.
  • Create a custom view.
  • Add a run description.
  • Tag and find runs.
  • Run search over your run history.
  • Cancel or fail runs.
  • Create child runs.
  • Monitor the run status by email notification.


If you're looking for information on monitoring the Azure Machine Learning service and associated Azure services, see How to monitor Azure Machine Learning. If you're looking for information on monitoring models deployed as web services, see Collect model data and Monitor with Application Insights.


You'll need the following items:

Monitor run performance

  • Start a run and its logging process

    1. Set up your experiment by importing the Workspace, Experiment, Run, and ScriptRunConfig classes from the azureml.core package.

      import azureml.core
      from azureml.core import Workspace, Experiment, Run
      from azureml.core import ScriptRunConfig
      ws = Workspace.from_config()
      exp = Experiment(workspace=ws, name="explore-runs")
    2. Start a run and its logging process with the start_logging() method.

      notebook_run = exp.start_logging()
      notebook_run.log(name="message", value="Hello from run!")
  • Monitor the status of a run

    • Get the status of a run with the get_status() method.

    • To get the run ID, execution time, and other details about the run, use the get_details() method.

    • When your run finishes successfully, use the complete() method to mark it as completed.

    • If you use Python's design pattern, the run will automatically mark itself as completed when the run is out of scope. You don't need to manually mark the run as completed.

      with exp.start_logging() as notebook_run:
          notebook_run.log(name="message", value="Hello from run!")

Run Display Name

The run display name is an optional and customizable name that you can provide for your run. To edit the run display name:

  1. Navigate to the runs list.

  2. Select the run to edit the display name in the run details page.

  3. Select the Edit button to edit the run display name.

Screenshot: edit the display name

Custom View

To view your runs in the studio:

  1. Navigate to the Experiments tab.

  2. Select either All experiments to view all the runs in an experiment or select All runs to view all the runs submitted in the Workspace.

In the All runs' page, you can filter the runs list by tags, experiments, compute target and more to better organize and scope your work.

  1. Make customizations to the page by selecting runs to compare, adding charts or applying filters. These changes can be saved as a Custom View so you can easily return to your work. Users with workspace permissions can edit, or view the custom view. Also, share the custom view with team members for enhanced collaboration by selecting Share view.

  2. To view the run logs, select a specific run and in the Outputs + logs tab, you can find diagnostic and error logs for your run.

Screenshot: create a custom view

Run description

A run description can be added to a run to provide more context and information to the run. You can also search on these descriptions from the runs list and add the run description as a column in the runs list.

Navigate to the Run Details page for your run and select the edit or pencil icon to add, edit, or delete descriptions for your run. To persist the changes to the runs list, save the changes to your existing Custom View or a new Custom View. Markdown format is supported for run descriptions, which allows images to be embedded and deep linking as shown below.

Screenshot: create a run description

Tag and find runs

In Azure Machine Learning, you can use properties and tags to help organize and query your runs for important information.

  • Add properties and tags

    To add searchable metadata to your runs, use the add_properties() method. For example, the following code adds the "author" property to the run:


    Properties are immutable, so they create a permanent record for auditing purposes. The following code example results in an error, because we already added "azureml-user" as the "author" property value in the preceding code:

    except Exception as e:

    Unlike properties, tags are mutable. To add searchable and meaningful information for consumers of your experiment, use the tag() method.

    local_run.tag("quality", "great run")
    local_run.tag("quality", "fantastic run")

    You can also add simple string tags. When these tags appear in the tag dictionary as keys, they have a value of None.

    local_run.tag("worth another look")
  • Query properties and tags

    You can query runs within an experiment to return a list of runs that match specific properties and tags.

    list(exp.get_runs(properties={"author":"azureml-user"},tags={"quality":"fantastic run"}))
    list(exp.get_runs(properties={"author":"azureml-user"},tags="worth another look"))

Cancel or fail runs

If you notice a mistake or if your run is taking too long to finish, you can cancel the run.

To cancel a run using the SDK, use the cancel() method:

src = ScriptRunConfig(source_directory='.', script='')
local_run = exp.submit(src)


If your run finishes, but it contains an error (for example, the incorrect training script was used), you can use the fail() method to mark it as failed.

local_run = exp.submit(src)

Create child runs

Create child runs to group together related runs, such as for different hyperparameter-tuning iterations.


Child runs can only be created using the SDK.

This code example uses the script to create a batch of five child runs from within a submitted run by using the child_run() method:

src = ScriptRunConfig(source_directory='.', script='')

local_run = exp.submit(src)

with exp.start_logging() as parent_run:
    for c,count in enumerate(range(5)):
        with parent_run.child_run() as child:
            child.log(name="Hello from child run", value=c)


As they move out of scope, child runs are automatically marked as completed.

To create many child runs efficiently, use the create_children() method. Because each creation results in a network call, creating a batch of runs is more efficient than creating them one by one.

Submit child runs

Child runs can also be submitted from a parent run. This allows you to create hierarchies of parent and child runs. You can't create a parentless child run: even if the parent run does nothing but launch child runs, it's still necessary to create the hierarchy. The statuses of all runs are independent: a parent can be in the "Completed" successful state even if one or more child runs were canceled or failed.

You may wish your child runs to use a different run configuration than the parent run. For instance, you might use a less-powerful, CPU-based configuration for the parent, while using GPU-based configurations for your children. Another common wish is to pass each child different arguments and data. To customize a child run, create a ScriptRunConfig object for the child run.


To submit a child run from a parent run on a remote compute, you must sign in to the workspace in the parent run code first. By default, the run context object in a remote run does not have credentials to submit child runs. Use a service principal or managed identity credentials to sign in. For more information on authenticating, see set up authentication.

The below code:

  • Retrieves a compute resource named "gpu-cluster" from the workspace ws
  • Iterates over different argument values to be passed to the children ScriptRunConfig objects
  • Creates and submits a new child run, using the custom compute resource and argument
  • Blocks until all of the child runs complete
# This script controls the launching of child scripts
from azureml.core import Run, ScriptRunConfig

compute_target = ws.compute_targets["gpu-cluster"]

run = Run.get_context()

child_args = ['Apple', 'Banana', 'Orange']
for arg in child_args: 
    run.log('Status', f'Launching {arg}')
    child_config = ScriptRunConfig(source_directory=".", script='', arguments=['--fruit', arg], compute_target=compute_target)
    # Starts the run asynchronously

# Experiment will "complete" successfully at this point. 
# Instead of returning immediately, block until child runs complete

for child in run.get_children():

To create many child runs with identical configurations, arguments, and inputs efficiently, use the create_children() method. Because each creation results in a network call, creating a batch of runs is more efficient than creating them one by one.

Within a child run, you can view the parent run ID:

## In child run script
child_run = Run.get_context()

Query child runs

To query the child runs of a specific parent, use the get_children() method. The recursive = True argument allows you to query a nested tree of children and grandchildren.


Log to parent or root run

You can use the Run.parent field to access the run that launched the current child run. A common use-case for using Run.parent is to combine log results in a single place. Child runs execute asynchronously and there's no guarantee of ordering or synchronization beyond the ability of the parent to wait for its child runs to complete.

# in child (or even grandchild) run

def root_run(self : Run) -> Run :
    if self.parent is None : 
        return self
    return root_run(self.parent)

current_child_run = Run.get_context()
root_run(current_child_run).log("MyMetric", f"Data from child run {}")

Monitor the run status by email notification

  1. In the Azure portal, in the left navigation bar, select the Monitor tab.

  2. Select Diagnostic settings and then select + Add diagnostic setting.

    Screenshot of diagnostic settings for email notification

  3. In the Diagnostic Setting,

    1. under the Category details, select the AmlRunStatusChangedEvent.
    2. In the Destination details, select the Send to Log Analytics workspace and specify the Subscription and Log Analytics workspace.


    The Azure Log Analytics Workspace is a different type of Azure Resource than the Azure Machine Learning service Workspace. If there are no options in that list, you can create a Log Analytics Workspace.

    Where to save email notification

  4. In the Logs tab, add a New alert rule.

    New alert rule

  5. See how to create and manage log alerts using Azure Monitor.

Example notebooks

The following notebooks demonstrate the concepts in this article:

Next steps